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Obstacle avoidance strategy for an autonomous surface vessel based on modified deep deterministic policy gradient

机译：Obstacle avoidance strategy for an autonomous surface vessel based on modified deep deterministic policy gradient

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摘要

In the present paper, a decision-making agent based on reinforcement learning is designed for establishing an obstacle avoidance strategy of an autonomous surface vessel (ASV). To solve the sparse feedback in obstacle avoidance issue, the Modified Deep Deterministic Policy Gradient (MDDPG) method is proposed in the present study, using an actor-network to generate actions from the state of the ASV and a critic-network to evaluate the behavior of the actor-network. The gradient descend method is applied to improve the networks. Memory pool modification, success pool modification and target network modification are developed to smooth the training process of the ASV. A comparative analysis based on numerical simulations are carried out using Deep Q Network (DQN) method, Modified Deep Q Network (MDQN) method and MDDPG method. The results demonstrate that the MDDPG method smooths and accelerates the learning process. Compared with the other two methods, the effectiveness and viability of the proposed method is significantly increased. Random obstacles tests are also conducted to confirm the applicability and generalization of the method.

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